HiBayES: Improving LLM evaluation with hierarchical Bayesian modelling

Accurately assessing the capabilities of advanced Large Language Models (LLMs) has become increasingly challenging. While historically LLMs were evaluated with question-answer benchmarks, the development of more complex capabilities means evaluations must evolve towards agentic and capability elicitation settings. These advanced evaluations involve complex scenarios and diverse types of inputs that test the model's capabilities in ways not captured by earlier, simpler benchmarks.

Conventional approaches to the statistical analysis of evaluation results often fall short when faced with these hierarchically structured and complex datasets, small sample sizes, and the inherently stochastic nature of LLM outputs. These new multi-layered and more intricate tasks require deeper analysis, robust uncertainty quantification and more nuanced understanding. Our new paper introduces HiBayES: a hierarchical Bayesian modelling framework designed to address these limitations through principled statistical methods.

AI evaluations face a fundamental challenge: as LLMs and agentic systems become increasingly sophisticated, the demand for reliable, nuanced benchmarks and evaluation metrics is soaring. Accurate assessment of model capabilities and predicting model behaviour in real-world use cases are not only crucial for driving further model development but also vital for improving model safety and safeguards in deployment.

These results underscore that the use of HiBayES provides principled uncertainty quantification and robust parameter estimation, even in low-data regimes with high levels of complexity.

HiBayES provides the building blocks for a more rigorous statistical approach to AI evaluation. Future extensions may include expanding predictive capabilities to estimate correlations across related tasks and domains, deepening our understanding of LLM performance and capability structures. The framework could also be used to reliably predict results from real-world applications like human-uplift studies based on findings from automated benchmarks.

HiBayES also scales effortlessly to comparing multiple LLMs or benchmarks without requiring statistical adjustments, providing comprehensive and reliable evaluations that support safer and more effective AI development. This approach will help prevent misguided data-driven decisions that could otherwise hinder progress in model development and safety.

The challenge lies in achieving precision, robustness, and principled uncertainty quantification for increasingly complex and hierarchically structured evaluation benchmarks. Conventional statistical approaches often fall short when dealing with the intricate and multidimensional nature of modern AI assessments. As a result, there is a pressing need for advanced methods that can provide reliable insights into model performance across diverse scenarios.

Compounding this issue is the economic cost of thoroughly testing performance across benchmarks and in complex, agentic settings – often costing hundreds of US Dollars in tokens to solve a single task. This economic reality necessitates working with smaller datasets, precisely where conventional approaches to data analysis are not robust.

While conventional analysis methods relying on simple statistical methods (e.g., t-tests) applied to pre-averaged data offer a quick means to answer basic questions, they often disregard the complex and hierarchical structure of evaluation data (Figure 1). Conventional analysis methods are known to overfit evaluation data and do not systematically quantify uncertainty across levels of the data hierarchy – both of which lead to under- or over-estimation of model capabilities. For organisations committed to deploying safe and effective AI systems, these statistical shortcomings represent a significant barrier to progress.